統計方法與資料分析

The course is organized around statistical topics. Each topic introduces concepts and terminology, develops the rationale for its methods, presents the mathematics and calculations for its methods, and gives examples supported by graphics and computer output, culminating in a writeup of conclusions. Our emphasis on graphical display of data is a distinguishing characteristic of this course. We show graphs, how to construct and interpret them, and how they relate to the tabular outputs that appear automatically when a statistical program “analyzes” a data set. The graphs are not automatic and so must be requested. Gaining an understanding of a data set is always more easily accomplished by looking at appropriately drawn graphs than by examining tabular summaries. In our opinion, graphs are the heart of most statistical analyses; the corresponding tabular results are formal confirmations of our visual impressions. The point-and-click technology of icons and menus is sometimes convenient for routine tasks. However, many interesting data analyses are not routine and therefore cannot be accomplished by pointing and clicking the icons provided by the program developers.

This course requires some previous studies of mathematics and statistics. We suggest some basic understanding of calculus including maximization or minimization of functions of one or two variables, and the ability to undertake definite integrations of elementary functions. We recommend acquired knowledge from an earlier statistics course, including a basic understanding of statistical measures, probability distributions, interval estimation, hypothesis testing, and simple linear regression.

The teaching materials are mainly extracted from the textbook. Please download the p.d.f. file of the textbook from our university library! Please do not print out the textbook whose copyright is by Springer! The projector and the blackboard will be used for this course.

作業部份: None 考試部份: It is required for every student to present a published paper by June 24, 2020. The paper should be chosen from "References" of the textbook. The presentation time should be about 15 minutes for each undergraduate student, about 30 minutes for each master student, and about 60 minutes for each Ph.D. student. Questions and answers are about 5 minutes for each undergraduate student, about 10 minutes for each master student, and about 20 minutes for each Ph.D. student. 評量部份: (1) Attendance at class. (2) Presentation of a published paper from "References" of the textbook.

• Chapter 1. Introduction and Motivation
• Chapter 2. Data and Statistics
• Chapter 3. Statistics Concepts
• Chapter 4. Graphs
• Chapter 5. Introductory Inference
• Chapter 6. One-Way Analysis of Variance
• Chapter 7. Multiple Comparisons
• Chapter 8. Linear Regression by Least Squares
• Chapter 9. Multiple Regression—More Than One Predictor
• Chapter 10. Multiple Regression—Dummy Variables, Contrasts, and Analysis of Covariance
• Chapter 11. Multiple Regression—Regression Diagnostics
• Chapter 12. Two-Way Analysis of Variance
• Chapter 13. Design of Experiments—Factorial Designs
• Chapter 14. Design of Experiments—Complex Designs
• Chapter 15. Bivariate Statistics—Discrete Data
• Chapter 16. Nonparametrics
• Chapter 17. Logistic Regression
• Chapter 18. Time Series Analysis

Richard M. Heiberger and Burt Holland (2015), Statistical Analysis and Data Display: An Intermediate Course with Examples in R, Second Edition, Springer.

地點

A416

時間

Every Thursday from 10:10 a.m. to 12:00 p.m.

聯絡方式

cchen@stat.nctu.edu.tw (03)571-2121 Ext. 31794 or (03)573-1794